Double balloon enteroscopy and capsule endoscopy for obscure gastrointestinal bleeding: An updated meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Uncertainty remains about the best test to evaluate patients with obscure gastrointestinal bleeding (OGIB). Previous meta-analyses demonstrated similar diagnostic yields with capsule endoscopy (CE) and double balloon enteroscopy (DBE) but relied primarily on data from abstracts and were not limited to bleeding patients. Many studies have since been published. Therefore, we performed a new meta-analysis comparing CE and DBE focused specifically on OGIB. METHODS: A comprehensive literature search was performed of comparative studies using both CE and DBE in patients with OGIB. Data were extracted and analyzed to determine the weighted pooled diagnostic yields of each method and the odds ratio for the successful localization of a bleeding source. RESULTS: Ten eligible studies were identified. The pooled diagnostic yield for CE was 62% (95% confidence interval [CI] 47.3-76.1) and for DBE was 56% (95% CI 48.9-62.1), with an odds ratio for CE compared with DBE of 1.39 (95% CI 0.88-2.20; P = 0.16). Subgroup analysis demonstrated the yield for DBE performed after a previously positive CE was 75.0% (95% CI 60.1-90.0), with the odds ratio for successful diagnosis with DBE after a positive CE compared with DBE in all patients of 1.79 (95% CI 1.09-2.96; P = 0.02). In contrast, the yield for DBE after a previously negative CE was only 27.5% (95% CI 16.7-37.8). CONCLUSIONS: Capsule endoscopy and double balloon enteroscopy provide similar diagnostic yields in patients with OGIB. However, the diagnostic yield of DBE is significantly higher when performed in patients with a positive CE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".